WP-GBDT: An Approach for Winner Prediction using Gradient Boosting Decision Tree

Predicting victories in video games from rich history of gameplay logs is a severe challenge to game developers. It is hard for humans to evaluate the real-time game situation and predict who will win the video game. In this paper, we propose an approach to this problem by following the sequence of machine learning steps which consist of feature engineering, feature selection, and model construction. We conduct a detailed analysis of the game logs and generate effective features from different granularity gameplay logs in the feature engineering phase. Then, we design a group based recursive feature elimination method for feature selection. In model construction, we present an ensemble approach that combines stacking and averaging for prediction to improve the generalization performance of models. The proposed approach achieves AUC scores of 0.8997 on the test set, which is the highest final score in the competition.